Integrating 4QS Factors to Enhance Learning Quality Using Machine Learning

The ultimate goal of any educational institution is to offer the best educational experience and impart knowledge effectively to students. Identifying students who need extra support and taking appropriate actions to enhance their performance is crucial in achieving this goal. This study explores the application of machine learning techniques to predict students' final grades based on their historical data. The proposed strategy integrates the 4QS Model, which focuses on intelligence, adversity, spirituality, emotional, and stress factors to address essential aspects of the academic environment. By targeting these key factors, the aim is to improve the teaching and learning environment, leading to enhanced student performance and future success. This approach enables educators to identify areas where students may struggle and tailor their teaching methods accordingly, resulting in a more effective and supportive educational experience for both faculty and students. A classification machine learning algorithm was employed to predict student performance.